Advertisement

Longitudinal Studies in Evidence-Based Software Engineering

  • Tracy Hall
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4336)

Abstract

Longitudinal studies (LS) generate particularly valuable empirical data. There are many reasons for this, most of which are related to the fact that LS are usually large scale. This allows for a range of rich data to be collected. It also means that the scale of data collected should enable statistically significant results to be generated. Furthermore there are also strong temporal aspects to longitudinal studies. These allow changes over time to be tracked which means that the life of a system can be better understood. It also means that the temporal aspects of process change can be identified. The scale and richness of data, collected over the lifetime of a development project, makes for a valuable empirical investigation.

References

  1. 1.
    Remsberg, K.: A life span approach to cardiovascular disease risk and aging: The Fels Longitudinal Study. Mechanisms of Aging & Development 124(3) (2003)Google Scholar
  2. 2.
    Maximilien, E.M., Williams, L.: Assessing test-driven development at IBM. In: Proc. of the 25th ICSE, Portland, Oregon, May 3-10, 2003, pp. 564–569. IEEE Computer Society, Washington (2003)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Tracy Hall

There are no affiliations available

Personalised recommendations